For years, the e-commerce sector has heard grand promises about AI transformation. Yet despite an explosion of SaaS platforms and Amazon listing optimization software, agency operators and major sellers continue to spend enormous amounts of time managing their stores by hand. Speaking privately with executives who run e-commerce agencies reveals a telling pattern: one AI tool identifies an issue, another suggests a solution, and a person must still decide what actually matters, implement the change, and verify the outcome. Managing multi-million-dollar Amazon portfolios across dozens of fragmented tools remains largely manual work, even with multiple "copilots" and "AI assistants" in the mix.

Too Much Software

E-commerce agencies don't suffer from a lack of software anymore. In many cases, they suffer from too much of it.

Cyril Golub, CEO & Founder of Jinnify.ai

Golub, an angel investor and former CEO and co-founder of Aheadworks (which he successfully exited in 2019), has observed the industry's technology landscape shift over more than twenty years. He notes that each new SaaS dashboard or AI copilot introduces fresh friction: another interface to master, another stream of recommendations to interpret, another tool requiring human connection to actual business results.

To understand where current AI commerce solutions fall short and what the next wave of AI infrastructure must accomplish, we spoke with e-commerce agency leaders and seasoned industry figures. Their insights map out four operational bottlenecks where existing tools stall, along with the blueprint for native AI systems that will replace them.

1. Catalog Firefighting Is Still a Human Job

When outsiders picture Amazon optimization, they envision keyword research or listing refinements. Ask someone actually running an agency what consumes most of their team's effort, and the answer is far less glamorous: constant catalog firefighting.

The biggest bottleneck for us is not PPC or research, it's Seller Central firefighting. Every day something breaks: suppressed ASINs, broken variations, catalog overwrites, 8541 errors, hazmat/document issues, etc. Tools like Data Dive are great at detecting problems, but they stop there. The actual fix often needs Seller Support/Brand Registry cases, multiple follow-ups, and someone who understands the account history.

Sam Shah, Founder of e-commerce agency Desverto

Amazon's infrastructure consists of flat files, Brand Registry overrides, and evolving category templates—a complex maze. Current SaaS solutions only handle half the work: they alert operators to problems but leave them to extinguish the fires.

The biggest unsolved bottleneck is turning fragmented catalog data into safe, correct, and scalable execution. Existing tools can identify suppressed listings, missing attributes, keyword gaps, stranded inventory, or variation issues, but they still leave sellers and agencies doing the hard part: determining the true root cause, knowing which edit is safe, navigating conflicting contribution data, and pushing changes without breaking a parent-child relationship, indexing, compliance status, or retail readiness.

Steven Pope, founder of My Amazon Guy

Exception Management

For a seller managing a large catalog or multiple client accounts, catalog work is not just 'fill in missing fields.' It is a constantly changing system involving flat files, Seller Central, brand registry, compliance documentation, category templates, image requirements, inventory feeds, and Amazon's sometimes inconsistent catalog rules. The actual bottleneck is exception management: thousands of small issues that each require context, judgment, evidence, and follow-through. Most SaaS tools surface the problem; they don't reliably own the resolution end to end.

Steven Pope

2. AI Tools Are Missing a Bigger Business Picture

Agency leaders also highlight that most tools concentrate on a single metric or task without accounting for the broader business context.

I tried multiple tools, but I think the main bottom line is that across most e-commerce SaaS and AI tools, the control layer is really missing. The way you want to optimize your campaigns is something they provide, but eventually, it doesn't connect with the bigger brand goals.

Adnan Aslam, CEO of UK-based Amazon agency Sellonics

Aslam argues that next-generation AI commerce tools must grasp the full range of operational dimensions at once: PPC budgets, inventory levels, profit margins, competitor positions, and where a brand sits in its lifecycle.

When there is a new launch and you want to rank for certain keywords, if you just command AI that we want to rank for those keywords, it's not even possible because you just launched: you don't have reviews, your conversion will be low, and you're competing with people who have been selling for decades. An ideal AI would need to understand what's possible for a certain brand at a certain stage and what the realistic limitations are.

Adnan Aslam

3. More Data Doesn't Mean Better Decisions

When Large Language Models became mainstream, many e-commerce software makers connected them to Amazon's Selling Partner API. Yet granting an LLM access to raw store data has not produced an ideal autonomous account manager.

In reality, feeding unstructured data into an LLM generates prompt noise and AI recommendations that can damage store performance. Frontier models lack built-in operational awareness. They cannot reliably separate minor log warnings from serious revenue threats or problems that might trigger catalog suspension.

Access is solved; we connect via MCP and can pull whatever we want from wherever we want. The problem is that everything is not what you need. A system that receives the full dump will use all of it… The real work is judgment: knowing which data matters for this account, this question, this moment, and leaving the rest out. Right now, that filtering is the human part of the job.

Klaidas Siuipys, Founder and CEO of AMZ Bees

A powerful LLM is not an e-commerce operator by itself. Using a frontier model to process every raw commerce record is like hiring a PhD to do arithmetic in Excel. You need deterministic software to prepare the data, persistent memory for the merchant's strategy, permissions around what the agent may change, execution rails, and then a feedback loop to see whether the decision was right.

Cyril Golub, CEO & Founder of Jinnify

4. The Gap Between AI Recommendations and Action

Today's AI commerce tools face their most fundamental limitation: they can suggest actions but often cannot execute them. Merchants must still log into Seller Central, paste recommendations manually, file support tickets, and return later to check whether the fix succeeded.

The biggest challenge with AI in commerce is integrating the AI-generated recommendation into the operational system around the business. If people still have to move between tools, validate outputs manually and execute changes themselves, you haven't fundamentally changed the operating model, but just accelerated one step within it. The real opportunity is to move from AI as a decision-support layer to AI as an execution layer, where agents can access the relevant context, operate within defined permissions, interact with systems of record and ultimately take action on behalf of the business.

Antons Sapriko, CEO of Scandiweb

We've spent two decades making it easier for businesses to sell online. The next phase is about making it easier for businesses to operate online. AI could become a new operational interface for merchants, but the underlying infrastructure will determine whether that shift actually gives merchants more leverage or simply creates another layer of dependency.

Ruslan Fazlyev, founder of e-commerce store platform Ecwid

Blueprint of an Ideal AI Commerce Solution for Sellers

The e-commerce operators we interviewed outlined what they believe an ideal AI system should accomplish to address these four challenges. Rather than serving as another dashboard that merely offers recommendations, the next generation should execute actions directly. It should also provide businesses varying levels of control based on risk, while fully grasping the relevant business context.

My ideal AI-native catalog system would operate like an experienced Amazon catalog operations team that has complete visibility, persistent memory and the ability to act. It would ingest every ASIN, SKU, parent-child variation, listing contribution, inventory position, case log, brand asset, compliance document, historical change and performance signal, then continuously prioritize work based on revenue risk, conversion upside, inventory exposure, and account-health risk.

Steven Pope, founder of My Amazon Guy

The key feature would be a 'catalog control center' that doesn't merely alert me that an ASIN is suppressed or a variation is broken. It would explain the cause in plain English, show the evidence, recommend the best fix, estimate the expected business impact, and either execute the fix automatically within predefined guardrails or prepare the exact file upload, case, or appeal for approval.

Steven Pope

The most important design principle would be confidence-based automation. Low-risk, reversible changes could be executed automatically; medium-risk changes could be queued for approval; and high-risk actions, such as variation restructuring, compliance changes, or edits to major ASINs, would require human signoff with a clear explanation of consequences.

Steven Pope

Detect, Diagnose, Fix, Verify, Escalate

The AI solution I'd want is something that can detect, diagnose, fix, verify, and escalate, with human approval for anything risky. If I could automate one workflow first, it would be suppressed or at-risk listing recovery. It happens daily, directly impacts revenue, and success is easy to measure.

Sam Shah

An ideal AI would need to understand what's possible for a certain brand at a certain stage and what the realistic limitations are. And based on that, it can give us, for example, a list of keywords that you can actually rank for.

Adnan Aslam, CEO of Sellonics

I don't think AI should be treated as a magician. It should be treated as a very capable assistant with a clear goal, clear boundaries and a measurable expected outcome.

Cyril Golub

Golub recently shared this perspective in a conversation with Claus Lauter on The Ecommerce Coffee Break, a podcast reaching millions of e-commerce professionals globally, where they explored how AI might reshape e-commerce and the future of online shopping.

The Future of E-Commerce is Agent-Driven

Agency leaders envision a transition from AI assisting sellers with individual tasks to AI agents actively managing portions of the commerce operation. On the buyer side, AI agents will determine which products to suggest. This makes it crucial for brands to organize their product data so machines can interpret it. On the seller side, AI agents will assume more routine responsibilities, adhering to guardrails, merchant permissions, and established rules.

I believe the future lies in conversational commerce, with AI as the layer between product and buyer. Instead of the shopper scanning a results page, an agent picks what fits. For sellers, it changes what optimization means. You stop competing only for a position in search results and start making sure a machine can correctly understand what your product is and who it is for.

Klaidas Siuipys

Over the next two to three years, I think AI commerce will shift Amazon sellers from manually operating listings and campaigns to managing intelligent systems that run much of the repetitive work. The winning sellers will not simply be the ones using AI to write better bullet points or generate images, but the ones whose product data, brand assets, operational workflows, and decision rules are structured well enough for AI agents to act on them safely.

Steven Pope

Discovery will move from search results to AI recommendations, making catalog quality the new SEO. Operations will become increasingly agent-driven, while humans focus on strategy, judgment and creativity.

Sam Shah

The Next E-Commerce Era

The victors in the AI commerce era will not be tools that simply generate product descriptions or flag catalog improvements. They will be the infrastructure that equips AI agents with the ability to grasp the business, make sound decisions, and execute actions securely.

Twenty years ago, starting an online store meant renting a server and installing an e-commerce platform. Until now, it meant opening a Shopify account. The next step will be as simple as saying, 'OK, Claude, start selling online for me.'

Cyril Golub, Founder of Jinnify

Source: The Next Web